Instructions to use rverma0631/Sanskrit_TTS_Rasa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rverma0631/Sanskrit_TTS_Rasa with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rverma0631/Sanskrit_TTS_Rasa", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio
How to use rverma0631/Sanskrit_TTS_Rasa with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for rverma0631/Sanskrit_TTS_Rasa to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for rverma0631/Sanskrit_TTS_Rasa to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for rverma0631/Sanskrit_TTS_Rasa to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="rverma0631/Sanskrit_TTS_Rasa", max_seq_length=2048, )
- Xet hash:
- 95ee0fe88a1aae1dbffe0236923ec7e6a1b17b0d040860b10efc4039f9f858b9
- Size of remote file:
- 389 MB
- SHA256:
- de3ceebff448e87452228376d86ffe496389e290070d301068839a86ededd6e6
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